scholarly journals Pemilihan Varietas Tebu Sesuai Lahan Menggunakan Metode Fuzzy Inferensi System Mamdani

2018 ◽  
Author(s):  
Daniel Alfa Puryono
Keyword(s):  
Sugar Cane ◽  
Fuzzy Inference ◽  
Target Selection ◽  
High Yield ◽  
Seed Selection ◽  
Transport Costs ◽  
Inference System ◽  
Sugarcane Varieties ◽  
Sugar Mills ◽  
Selection Of

In line with the government's program to increase the yield and quality in the field of agriculture one of them is able to self-sufficiency. Thus the increase in agriculture cane ranging from seed selection in accordance with the land until the processing of sugar cane into sugar ready for sale with its main partners sugarcane farmers is a must. Indeed there are many varieties of seed cane but there are also many varieties of sugarcane that do not reach the targets with a maximum sugar production because it does not conform with the land at the time of planting, so that farmers suffered losses as well as sugar mills also can not result in the production of sugar in accordance with the target. Selection of sugarcane varieties in accordance with the conditions of land and soil types is very important to improve farm productivity and farm land. Many ways to define the appropriate criteria to obtain varieties with high yield and with a low tonnage in order to produce more sugar at once can reduce transportation costs and cut transport costs. Because sugarcane varieties largely determines the success of the production of sugar in the plant because basically sugar made in the garden, one way of selecting appropriate seeds whith fuzzy logic. This study aims to determine the varieties of sugar cane in accordance with the land by using a model of Mamdani Fuzzy Inference System or often also known as min-max method. Analysis of the system to get the output is done in several steps, namely the establishment of fuzzy sets, Establishment of rules, rules of composition determination, discernment (defuzzification). While the selection of appropriate varieties of sugar cane land based species and varieties of sugarcane, soil, drainage, climate such as rainfall and temperature, sunlight and air speed. The results of this study shows the results obtained proved to be better and more natural. Researchers made this system is expected to help cane farmers and sugar mills in making more accurate decisions to be in recommendations to farmers and overseers field. Because the report is valid and there is no duplication or manipulation of data.

IMPLEMENTASI PENENTUAN ARSITEKTUR MASJID MODERN DI ACEH MENGGUNAKAN PEMODELAN SISTEM CERDAS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Soraya Masthura Hasan ◽  
T Iqbal Faridiansyah
Keyword(s):  
Fuzzy Inference System ◽  
Architectural Design ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Selection Process ◽  
System Modeling ◽  
Inference System ◽  
Normal Limits ◽  
Roof Structure ◽  
Selection Of

Mosque architectural design is based on Islamic culture as an approach to objects and products from the Islamic community by looking at their suitability and values and basic principles of Islam that explore more creative and innovative ideas. The purpose of this system is to help the team and the community in seeing the best mosque in the top order so that the system can be used as a reference for the team and the community. The variables used in the selection of modern mosques include facilities and infrastructure, building structure, roof structure, mosque area, level of security and facilities. The system model used is a fuzzy promethee model that is used for the modern mosque selection process. Fuzzy inference assessment is used to determine the value of each variable so that the value remains at normal limits. Fuzzy values will then be included in promethee assessment aspects. The highest promethee ranking results will be made a priority for the best mosque ranking. This fuzzy inference system and promethee system can help the management team and the community in determining the selection of modern mosques in aceh in accordance with modern mosque architecture. Intelligent System Modeling System In Determining Modern Mosque Architecture in the City of Aceh, this building will be web based so that all elements of society can see the best mosque in Aceh by being assessed by all elements of modern mosque architecture.Keywords: Fuzzy inference system, Promethe, Option of  Masjid

scholarly journals PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (Aplikasi pada Data Harga Beras Kualitas Rendah di Indonesia Periode Januari 2013 – Februari 2019)

2019 ◽  
Vol 8 (4) ◽  
pp. 451-461
Author(s):  
Khusnul Umi Fatimah ◽  
Tarno Tarno ◽  
Abdul Hoyyi
Keyword(s):  
Lagrange Multiplier ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Lagrange Multiplier Test ◽  
Inference System ◽  
Input Selection ◽  
Lm Test ◽  
Rice Prices ◽  
Selection Of

Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and  with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting

Machine Fault Detection Using Neuro-Fuzzy Inference System and Genetic Algorithms

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Genetic Algorithms ◽  
Fault Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Vibration Data ◽  
Neuro Fuzzy ◽  
Machine Fault ◽  
The Time Domain ◽  
Selection Of

A study is presented to show the performance of machine fault detection using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs), termed here as GA-ANFIS. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GA-ANFIS for two class (normal or fault) recognition. The number and the parameters of membership functions used in ANFIS along with the features are selected using GAs maximizing the classification success. The results of fault detection are compared with GA based artificial neural network (ANN), termed here as GA-ANN. In GA-ANN, the number of hidden nodes and the selection of input features are optimized using GAs. For each trial, the GA-ANFIS and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GA-ANFIS and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers (ANFIS and ANN) with GA based selection of features and classifier parameters.

Adoption of new sugarcane varieties by the non-miller-planters in Mauritius: the importance of on-farm trials

1999 ◽  
Vol 35 (4) ◽  
pp. 417-425 ◽  
Author(s):  
K. P. PILLAY
Keyword(s):  
Sugar Industry ◽  
Sugarcane Varieties ◽  
Additional Information ◽  
Industry Research ◽  
Research Activities ◽  
Extension Activity ◽  
Sugar Mills ◽  
New Varieties ◽  
On Farm ◽  
Selection Of

One of the major research activities of the Mauritius Sugar Industry Research Institute (MSIRI) is the breeding and selection of sugarcane varieties adapted to the different agro-climatic environments. Most of the on-farm experimentation is carried out in the fields of miller-planters (planters who own the sugar mills), where resources in terms of land, labour and other inputs are usually easily available. The non-miller-planters are operating in environments which are not always similar to those of the miller-planters. There is a need for on-farm experimentation under these conditions in order to improve productivity. Variety trials, observation plots and industrial trials to compare commercial varieties with promising and newly released ones have been established on-farm in order to improve the adoption rate of new varieties. The paper elaborates on the objectives of this major extension activity during the last decade. A major inference is that the planters obtain first-hand information on promising varieties prior to their release for commercial plantation, hence influencing their decision for adoption or rejection. Moreover, additional information on the performance and behaviour of these promising varieties is made available to research staff.

scholarly journals REEs associated with carbonatite-alkaline complexes in western Rajasthan, India: exploration targeting at regional-scale

2021 ◽  
Author(s):  
Malcolm Aranha ◽  
Alok Porwal ◽  
Manikandan Sundaralingam ◽  
Ignacio González-Álvarez ◽  
Amber Markan ◽  
...  
Keyword(s):  
North America ◽  
Fuzzy Inference ◽  
Regional Scale ◽  
Inference System ◽  
Near Surface ◽  
Target Delineation ◽  
East European ◽  
The Baltic ◽  
Alkaline Complexes ◽  
Selection Of

Abstract. A two-stage fuzzy inference system (FIS) is applied to prospectivity modelling and exploration-target delineation for REE deposits associated with carbonatite-alkaline complexes in western part of the state of Rajasthan in India. The design of the FIS and selection of the input predictor map are guided by a generalised conceptual model of carbonatite-alkaline-complexes-related REE mineral systems. In the first stage, three FISs are constructed to map the fertility and favourable geodynamic settings, favourable lithospheric architecture, and favourable shallow crustal (near-surface) architecture, respectively, for REE deposits in the study area. In the second stage, the outputs of the above FISs are integrated to map the prospectivity of REE deposits in the study area. Stochastic and systemic uncertainties in the output prospectivity maps are estimated to facilitate decision making regarding the selection of exploration targets. The study led to identification of prospective targets in the Kamthai-Sarnu-Dandeli and Mundwara regions, where project-scale detailed ground exploration is recommended. Low-confidence targets were identified in the south of the Siwana ring complex, north and northeast of Sarnu-Dandeli, south of Barmer, and south of Mundwara. Detailed geochemical sampling and high-resolution magnetic and radiometric surveys are recommended in these areas to increase the level of confidence in the prospectivity of these targets before undertaking project-scale ground exploration. The prospectivity-analysis workflow presented in this paper can be applied to delineation of exploration targets in geodynamically similar regions globally such as Afar province (East Africa), Paraná-Etendeka (South America and Africa), Siberian (Russia), East European Craton-Kola (Eastern Europe), Central Iapetus (North America, Greenland and the Baltic region), and the Pan-superior province (North America).

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